标题： 在绝热量子计算机上的平衡k-均值聚类 显示英文标题

标题： Balanced k-Means Clustering on an Adiabatic Quantum Computer

摘要： 绝热量子计算机是一个有前途的平台，可用于近似解决具有挑战性的优化问题。 我们提出一种量子方法，在D-Wave 2000Q绝热量子计算机上解决平衡的$k$-means聚类训练问题。 现有的经典方法在大数据集上扩展性较差，并且只能保证局部最优解。 我们表明，我们的量子方法更好地针对训练问题的全局解，同时在大数据集上实现了更好的理论可扩展性。 我们在一些小问题上测试了我们的量子方法，并观察到其聚类性能与最佳经典算法相似。 显示英文摘要

摘要： Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems. We present a quantum approach to solving the balanced $k$-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer. Existing classical approaches scale poorly for large datasets and only guarantee a locally optimal solution. We show that our quantum approach better targets the global solution of the training problem, while achieving better theoretic scalability on large datasets. We test our quantum approach on a number of small problems, and observe clustering performance similar to the best classical algorithms.